code: def _parse_fill(
      fill: Optional[Union[float, List[float], Tuple[float, ...]]],
      img: Image.Image,
      name: str = "fillcolor",
  ) -> Dict[
      str, Optional[Union[float, List[float], Tuple[float, ...]]]
  ]:
      # Process fill color for affine transforms
      num_channels = get_image_num_channels(img)
      if fill is None:
          fill = 0
      if isinstance(fill, (int, float)) and num_channels > 1:
          fill = tuple([fill] * num_channels)
      if isinstance(fill, (list, tuple)):
          if len(fill) == 1:
              fill = fill * num_channels
          elif len(fill) != num_channels:
              msg = "The number of elements in 'fill' does not match the number of channels of the image ({} != {})"
              raise ValueError(msg.format(len(fill), num_channels))
  
          fill = tuple(fill)  # type: ignore[arg-type]
  
      if img.mode != "F":
          if isinstance(fill, (list, tuple)):
              fill = tuple(int(x) for x in fill)
          else:
              fill = int(fill)
  
      return {name: fill}
  
imports: import numbers
  from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from PIL import Image, ImageEnhance, ImageOps
label_desc: Use image node_id 15 for calling a global function with example usage: _parse_fill(fill, img, name) and returns: dict